Professional Analysts Research Articles

Investigating Professional Analyst Strategies in Immersive Space to Think.

Existing research on sensemaking in immersive analytics systems primarily focuses on understanding how users complete analysis within these systems with quantitative and qualitative datasets. However, these user studies mainly concentrate on understanding analysis styles and methodologies from a predominantly novice user study population. While this approach provides excellent initial insights into what users may do within IA systems, it fails to address how professionals may utilize an immersive analytic system for analysis tasks. In our work, we build upon an existing immersive analytics concept - "Immersive Space to Think" to understand how professional user populations differ from novice users in immersive analytic system usage. We conducted a user study with 11 professional intelligence analysts who completed three analysis sessions each. Using our results from this study, we provide deep analysis into how professional users complete sensemaking within immersive analytic systems, compare our findings to previously published findings with a novice user population, and provide insights into how to develop better IA systems to support the professional analyst's strategies within these systems.

ОСОБЛИВОСТІ ФУНКЦІОНУВАННЯ ВАЛЮТНОГО РИНКУ УКРАЇНИ В УМОВАХ ЕКОНОМІЧНОЇ НЕВИЗНАЧЕНОСТІ

In the era of globalization and the advancement of information technology, Ukraine’s foreign exchange market encounters economic uncertainty, particularly amid efforts to overcome the repercussions of the COVID-19 pandemic and the challenges posed by a full-scale armed invasion by Russia. This situation introduces a range of short-term and long-term obstacles, notably disrupting established trade routes and supply chains, impacting the balance of imports and exports, and placing significant strain on the foreign exchange market among other areas. This article aims to analyze the current peculiarities of the domestic foreign exchange market’s operation amidst economic uncertainty, focusing on the impact of governmental and regulatory actions on its dynamics, especially throughout 2022-2023. It delves into the regulatory measures enacted to stabilize the foreign exchange market under martial law conditions. The National Bank of Ukraine (NBU) has implemented several countermeasures in response to these challenges, including covering 37% of the currency supply, which resulted in a reduction in daily transactions and an enhanced role in market equilibrium. The measures also include restrictions on capital transactions and the facilitation of international assistance to satisfy foreign exchange demands. The future outlook of the foreign exchange market is contingent upon economic recuperation, foreign investments, macroeconomic policies, geopolitical risks, and regulatory frameworks. The significance of domestic reforms and international support is underscored as pivotal. It is also noted that global economic tendencies will influence Ukraine’s foreign exchange market in the aftermath of the conflict. This study is aimed at providing insights for professional analysts of the foreign exchange market, governmental bodies, and international financial institutions. Future investigations will explore the implications of the NBU’s policies on the foreign exchange market and their potential to foster economic recovery in Ukraine amidst prevailing challenges and threats.

Strategi Dan Policy Manajemen Untuk Menerjemahkan Aktivitas Pengadaan Material Dan Forecasting Material

This section presents order growth projections based on professional analyst consensus estimates to help analyze procurement orders in the inventory control section. Inventory control in a company is closely related to the activity of collecting data about activities and transactions in and out of a company's goods. The basic concept of inventory is that every company, whether it is a trading company or factory company or service company, always holds inventory. Without inventory, entrepreneurs will be faced with the risk that their company at some point will not be able to fulfill the desires of customers who need or request goods/services. Inventory is held if the expected profit from the inventory should be greater than the costs it incurs. By designing a better system, it is hoped that it can help and facilitate the ongoing system process, making it easier to process data, including processing, compiling, storing and manipulating data, which ultimately produces accurate data that can be used for company needs. For this reason, an information system is needed to overcome this problem, a dual information system that can inform existing data correctly and accurately.
 

Impact assessment of the introduction of COVID-19 quarantine restrictions on financial performance of small and medium enterprise sector of the Russian Federation

The deterioration of the economic situation in the country is certainly reflected in the financial performance of small and medium enterprise sector (hereinafter referred to as SME sector). The article attempts to study the degree of impact of forced urgent measures introduced by state authorities to contain the spread of coronavirus infection on financial performance of the sector. The authors conducted an overview study of scientific publications written by leading professional analysts on the problems of the economic situation in the Russian entrepreneurial segment, as well as on the relevance and effectiveness of government measures to support small and medium-sized enterprises. The assessment of changes in the final performance indicators of each of the three SME sector elements (micro, small and medium enterprises) over the past three years is given. When studying the dynamics of the components of the balanced financial result, horizontal analysis was used, alongside with the method of absolute differences, calculation of growth rates, and index method. The made analysis revealed a sharp deterioration in financial performance during the most severe quarantine restrictions in 2020 and an improvement in the economic situation after their partial abolition in 2021. It was noted that the risk of mass bankruptcy of enterprises in the SME sector was compensated by large-scale state support. It was determined that the protective restrictions imposed to localise the epidemic have aggravated the existing problems of small and medium-sized businesses. The article shows that the main risks of the studied segment will persist in the near future. In this regard, it is necessary to improve the tools of state regulation of SME sector development with consideration to the accumulated experience of anti-crisis management.

Інструменти мотивації Офісу управління проєктами

The modern project management environment is becoming more and more volatile under the influence of exogenous and endogenous factors. The key factor in this context is motivation, which has the potential to increase its application in project management. The aim of the article is to analyze the motivation strategies that can be implemented by the project management office to increase the success of projects. The importance of motivation in the context of project management is considered, with an emphasis on how motivational strategies can affect the productivity and effectiveness of teams. The importance of the project management office in the development of team motivation to achieve success in project management in conditions of increased volatility of the business environment is emphasized. According to McKinsey, 37% of situations leading to the failure of a project are due to changes in its goals, which directly or indirectly affects motivation. Various methods of motivation used by the project management office, including intrinsic and extrinsic motivation, and their impact on the behavior of employees are considered. Attention is paid to the psychological aspects of motivation, the role of cultural and individual differences. Particular attention is paid to the use of metrics for quantitative project management, which provides a more structured and objective approach to evaluating and improving various aspects of the project, including motivation. An analysis of the importance of creating a favorable environment for motivation and the use of a strategic approach to the implementation of motivational strategies is carried out. The proposed research can be useful for professional analysts and researchers, companies, non-profit organizations and authorities who seek to strengthen the motivation component of the project management office. Prospects for further research include the development of a KPI system taking into account the main organizational goals and, on the basis of their construction, a motivation system by the project management office.

Connecting Cross‐Border Market Participants: The Intermediary Role of International Analysts in Global Capital Markets

AbstractAlthough the analyst profession has rapidly expanded internationally, very few studies have examined the distinct role of international analysts in the global economy. Motivated to address this need, we examine the role of international analysts as key intermediaries connecting local firms with foreign investors. Specifically, we investigate whether and under what circumstances the international analyst community in a host country influences foreign equity investment in the country. Our analyses of foreign portfolio investment inflows in 57 countries from 1987 to 2007 demonstrate that the international analyst community plays an essential role in facilitating cross‐border equity investment by mitigating foreign investors' information challenges. We also find that the extent of such influence is further affected by the heterogeneity of the host country's information environment and by that of the international analyst community. These findings extend the extant knowledge of analysts, information intermediaries, cross‐border capital investment, and global professions.

PERCEPTION OF UNCERTAINTY AND GREEN INNOVATION: MACHINE LEARNING EVIDENCE FROM PUBLICLY LISTED FIRMS IN CHINA

Using a unique panel dataset consisting of 2997 Chinese manufacturing firms publicly listed in the A-share market between 2003 and 2020, we examine whether and to what extent a firm’s perception of uncertainty affects green innovation. After integrating textual analysis with a machine learning approach to measure perception of uncertainty, we find that a firm’s perception of environmental uncertainty negatively affects the number of green patents submitted or approved. The negative effect is weaker for firms followed by more professional analysts, operating in more competitive markets, or located in regions with better institutional settings. In addition, there is significant heterogeneity in the negative effect between non-state-owned versus state-owned firms as well as polluting versus non-polluting firms. The results are robust to different measures of green innovation and perception of uncertainty, and after addressing for potential endogeneity problem. Our study contributes to the literature on behavioral environmental economics by demonstrating that it is not only the environment uncertainty but also how firms perceive the uncertainty matters for green innovation and corporate social responsibility.

Data Analysis Training Using Python at JNE

The need for the Data Analyst profession is very high, triggered by the development of digital technology. Thus, Matana University through the statistics study program feels it is important to carry out training activities in the field of data analysis. The aim is to provide benefits to companies that are partners in the implementation of this Community Service (PKM) is JNE. Participants who take part in the training are limited to twenty people, so that the quality of learning is more optimal with the practicum learning method. The training instructor also involves at least two i-students as assistants in each meeting. This training was carried out in collaboration with the Learning Center team at JNE. The implication of the training is a change in the ability of data analysis for the participants, even ordinary people, and further training will soon be held.

Finding Their Data Voice: Practices and Challenges of Dashboard Users.

Dashboards are the ubiquitous means of data communication within organizations. Yet we have limited understanding of how they factor into data practices in the workplace, particularly for data workers who do not self-identify as professional analysts. We focus on data workers who use dashboards as a primary interface to data, reporting on an interview study that characterizes their data practices and the accompanying barriers to seamless data interaction. While dashboards are typically designed for data consumption, our findings show that dashboard users have far more diverse needs. To capture these activities, we frame data workers' practices as data conversations: conversations with data capture classic analysis (asking and answering data questions), while conversations through and around data involve constructing representations and narratives for sharing and communication. Dashboard users faced substantial barriers in their data conversations: their engagement with data was often intermittent, dependent on experts, and involved an awkward assembly of tools. We challenge the visualization and analytics community to embrace dashboard users as a population and design tools that blend seamlessly into their work contexts.

Users’ trust in black-box machine learning algorithms

PurposeThis paper aims to investigate whether professional data analysts’ trust of black-box systems is increased by explainability artifacts.Design/methodology/approachThe study was developed in two phases. First a black-box prediction model was estimated using artificial neural networks, and local explainability artifacts were estimated using local interpretable model-agnostic explanations (LIME) algorithms. In the second phase, the model and explainability outcomes were presented to a sample of data analysts from the financial market and their trust of the models was measured. Finally, interviews were conducted in order to understand their perceptions regarding black-box models.FindingsThe data suggest that users’ trust of black-box systems is high and explainability artifacts do not influence this behavior. The interviews reveal that the nature and complexity of the problem a black-box model addresses influences the users’ perceptions, trust being reduced in situations that represent a threat (e.g. autonomous cars). Concerns about the models’ ethics were also mentioned by the interviewees.Research limitations/implicationsThe study considered a small sample of professional analysts from the financial market, which traditionally employs data analysis techniques for credit and risk analysis. Research with personnel in other sectors might reveal different perceptions.Originality/valueOther studies regarding trust in black-box models and explainability artifacts have focused on ordinary users, with little or no knowledge of data analysis. The present research focuses on expert users, which provides a different perspective and shows that, for them, trust is related to the quality of data and the nature of the problem being solved, as well as the practical consequences. Explanation of the algorithm mechanics itself is not significantly relevant.

Analysts’ stock ratings and the predictive value of news and Twitter sentiment

ABSTRACT This study investigates the relationship between the stock ratings of professional analysts and company-level sentiment. I find that investor sentiment expressed in tweets and news articles displays a positive relationship with professional analysts’ stock ratings and that the sentiment conveyed through tweets has a stronger effect than the sentiment from conventional news articles. Furthermore, the effect of sentiment derived from tweets and news articles on analysts’ ratings is stronger when the sentiment valence is the same (negative or positive) and weaker when the sentiment valence differs. The findings shed light on the link between investor sentiment and analysts’ stock ratings by demonstrating that analysts’ stock ratings are influenced by investor sentiment.

Peer effects in professional analysts’ choice of their portfolio of companies

Analysts’ earnings forecasts are well known to be affected by herding behavior. This article provides evidence that herding might also play a role in analysts’ choice of coverage portfolio. Using the I/B/E/S database we show that there are strong peer effects at work in analysts’ choice of their portfolio of companies that go beyond specialization in teams and for certain sectors. To identify the factors driving these findings, we adopt a model of link formation within the bipartite network defined by analyst-company pairs. The strongest influence on imitation comes from the performance of other analysts. In contrast, professional experience and other individual characteristics seem to exert only a minor influence.

Nonfinancial Corporate Social Responsibility Reporting and Firm Value: International Evidence on the Role of Financial Analysts

Using a large international sample of 24,293 observations from 3,991 unique firms in 56 countries, we examine the role of financial analysts in the relationship between voluntary corporate social responsibility (CSR) reporting and firm value. We find that after controlling for firms’ CSR performance ratings and other factors, voluntary CSR reporting increases firm value in countries worldwide, and analysts strengthen the positive relationship between CSR reporting and firm value. More importantly, our results show that the positive role of analysts in the relationship between CSR reporting and firm value varies with country-level institutional characteristics, such as the level of investor protection, the development of capital markets and the analyst profession, and stakeholder orientation. Furthermore, the positive role of financial analysts varies according to CSR reporting characteristics, including CSR reporting assurance, choice of assurer, CSR reporting coverage, CSR reporting quantity, and length of CSR report. We also find that financial analysts can strengthen the positive association between CSR reporting and firm value for CSR disclosures published in all types of media (standalone, annual report-based, and web-based CSR disclosures). Overall, our results present global evidence that shows the important role of financial analysts in improving the valuation implications of voluntary CSR reporting.

Relevance Analysis of Systems Analysis and Design Courses With System Analyst Skill Needs

Entering the era of the industrial revolution 4.0, there is an increasing need for digital workers. However, based on available data, Indonesia's digital talent is still very lacking and there is a mismatch between the supply of labor and the needs of the industry which has led to an increase in the unemployment rate in Indonesia. This gap must be a concern of educational institutions, especially universities, to provide educational designs that are in accordance with industry needs. Systems analysis and design courses play an important role in the development of digital skills, especially for the System Analyst profession which is much needed at this time. Based on the various skills needed by the company for System Analysts, the relevance of the curriculum analysis and system design courses used in universities in Indonesia will be reviewed. This study uses content analysis methods to analyze information based on system analyst job advertisements and course lesson plans (RPS) for systems analysis and design courses. The data testing method is carried out using a correlation test which shows that there is no relationship between the analysis and system design courses with the needs of systems analyst skills in the industry today. With the Cartesian diagram, it is known that there are two skill categories that need to be prioritized for improvement in the learning plan for the system analysis and design course, namely testing (SIT and UAT) and basic programming.

Аналитическое обеспечение моделирования бизнес-процессов в организации

Background. 
 Business analysis is not widely used in today's enterprises, and the business analyst profession is relatively recent. At the same time, business analytics, and in particular the modeling of business processes, plays a huge role in managing the activities of an organization. Understanding the relationship between business processes provides significant opportunities for analyzing and planning business changes.
 The modern market is not a market of quantity, but of the quality of the services provided, which is why there is a need to analyze existing business processes in order to form an effective enterprise management system. As a result of the analysis, the enterprise forms a system of indicators for evaluating the effectiveness of key business processes with the aim of their subsequent optimization, replacement or reengineering.
 In general, the activity of the enterprise can be reduced to the identification of internal and external events that affect the efficiency of business processes.
 The relevance of the topic lies in the fact that the timely detection of deviations from the requirements that affect the efficiency of existing business processes and their optimization allow the business to make effective decisions for its continued existence.
 Methods.
 The article discusses the main methods, algorithm and tools for analyzing business processes in the activities of enterprises.
 Result.
 The article discusses the methods of business process analysis, defines the problems of implementing business process analysis methods, discusses the procedures and algorithms used to address issues related to identifying problematic business processes.

Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data

ABSTRACTWe use machine learning methods and high‐dimensional detailed financial data to predict the direction of one‐year‐ahead earnings changes. Our models show significant out‐of‐sample predictive power: the area under the receiver operating characteristics curve ranges from 67.52% to 68.66%, significantly higher than the 50% of a random guess. The annual size‐adjusted returns to hedge portfolios formed based on the prediction of our models range from 5.02% to 9.74%. Our models outperform two conventional models that use logistic regressions and small sets of accounting variables, and professional analysts’ forecasts. Analyses suggest that the outperformance relative to the conventional models stems from both nonlinear predictor interactions missed by regressions and the use of more detailed financial data by machine learning.

Perceptions of transfer of training: interaction of a continuous learning culture with network diversity, network strength, and network size

ABSTRACT We theorize how a continuous learning culture relates to perceptions of transfer of training moderated by network size, network diversity, and network strength. We collected data from a group of professional analysts from a multinational organization. Results reveal that both network size and network diversity interacting with a continuous learning culture measured by both competitiveness and organizational support explain a significant proportion of variance in perceptions of transfer of training. As expected, large network size along with high competitiveness and high organizational support are associated with an increase in perceptions of transfer of training. In addition, high network diversity along with high competitiveness and high organizational support are associated with an increase in perceptions of transfer of training. However, and contrary to expectations, we also found that perceptions of transfer of training were highest with high network diversity, high competitiveness, and low organizational support. High network diversity appears to reduce the need for organizational support in relation to perceptions of transfer of training with a sample of employees from around the world.

Review of the National Experience of Measuring Gender Inequality in Ukraine, 2009–2021. Part I. Government Practice of Gender Measurements

The review presents the national practice of measuring gender equality in Ukraine since 2009 and identifies gaps in the tools, methods and indicators used for this measurement.
 Due to the difference in the powers and responsibilities of governmental and non-governmental sectors, the first part of the review presents state practices for measuring gender inequality, and the second part presents relevant initiatives by non-governmental communities and donors.
 It is argued that the state gender measurements in Ukraine had two active periods: 2009-2011 and 2019-2021. The first of them introduced the practice of preparing gender portraits of regions, which became widespread; and prepared a basic overview of the feasibility of creating a national system of gender statistics of Ukraine and its indicators. In the second of them, the government carried out work on standardizing the gender profiles of 25 regions of Ukraine; and the selection of gender-disaggregated indicators for nationwide monitoring of gender equality.
 It has been established that the longest-running and most widespread national practice of gender dimension in Ukraine is gender portraiture, which was conducted in more than 2/3 of Ukraine’s regions and later spread to cities and local communities. However, tools for gender profiling and reporting on sets of advanced indicators have not become widespread.
 It is established that the predominant type of gender measurement tool is a set of indicators, numbering about 30, grouped in 4-6 thematic areas. This set is usually determined from the available data of the State Statistics Service and is available for use in the absence of professional gender analysts. Attempts to use and disseminate more detailed sex measurement tools have failed. According to the levels of the gender measurement, national and local measurements were found to be preferred. There were no precedents for sectoral gender measurement and attempts to develop sectoral gender statistics. It is concluded that this is a gap that needs to be addressed.
 It was found that all measurements, except for the statistical collection ‘Women and Men in Ukraine” were irregular. Measurements on the same list of indicators were carried out once or never.
 It is concluded that there is a lack of coordination of measurement practices at the national and local levels, even if it was carried out by the same responsible state body.
 The review was prepared within the framework of the Women’s Rights Monitoring Initiative project, implemented in 2019-2021 by the Kyiv Institute for Gender Studies with the support of the Swiss Embassy in Ukraine.

The Selection and Training Framework for Managers in Business Innovation and Transformation Projects

The riskiest factor in transforming a traditional business environment (BE) into a lean and automated BE is the role of the profile and the corresponding TOGAF managerial recommendations of the business and (e-)business transformation managers (BTM); the influence they have on the concrete implementation phase of business transformation projects (BTP). The basic profile and “The OpenGroup Architecture Framework” (TOGAF)[66] managerial recommendations of such a business transformation manager has not been sufficiently researched in a holistic manner in order to hammer the BTM’s profile and the corresponding TOGAF managerial recommendations; and that is the main goal of the authors’ research [52][37][38]. In fact, currently there are no TOGAF managerial recommendations and educational curricula for such BTM profiles. This research paper deals with the TOGAF managerial recommendations for the BTM selection and education; who has to manage the technical implementation phase of complex business transformation projects; knowing that the BTPs’ implementation phase is the major cause of very high failure rates [20][21]. The implementations of such business transformation projects require a specific set of enterprise business architecture knowledge. The authors have based their research on the main fact that only around 12% of business organizations successfully terminate innovationrelated business transformations projects [8]. “We know that those organizations that are consistently successful at managing innovation-related changes outperform their peers in terms of growth and financial performance” [7]. Therefore, there is an essential need for more research on the BTMs’ profiles and the TOGAF related managerial recommendations. This research project presents an original set of factors and fulfills the need for an efficient tree reasoning model, in the form of a real world framework and recommendations, which affect the BTM’s selection techniques. BTM selectors, professional analysts, project managers, auditors and advanced computer science students, will benefit from this research project.

Untidy Data: The Unreasonable Effectiveness of Tables.

Working with data in table form is usually considered a preparatory and tedious step in the sensemaking pipeline; a way of getting the data ready for more sophisticated visualization and analytical tools. But for many people, spreadsheets - the quintessential table tool - remain a critical part of their information ecosystem, allowing them to interact with their data in ways that are hidden or abstracted in more complex tools. This is particularly true for data workers [61], people who work with data as part of their job but do not identify as professional analysts or data scientists. We report on a qualitative study of how these workers interact with and reason about their data. Our findings show that data tables serve a broader purpose beyond data cleanup at the initial stage of a linear analytic flow: users want to see and "get their hands on" the underlying data throughout the analytics process, reshaping and augmenting it to support sensemaking. They reorganize, mark up, layer on levels of detail, and spawn alternatives within the context of the base data. These direct interactions and human-readable table representations form a rich and cognitively important part of building understanding of what the data mean and what they can do with it. We argue that interactive tables are an important visualization idiom in their own right; that the direct data interaction they afford offers a fertile design space for visual analytics; and that sense making can be enriched by more flexible human-data interaction than is currently supported in visual analytics tools.